Xinyu Li, Dongyang Yao, Xuechao Pan, Jonathan Johannaman, JaeWon Yang, Rachel Webman, Aleksandra Sarcevic, Ivan Marsic, Randall S Burd
{"title":"Activity Recognition for Medical Teamwork Based on Passive RFID.","authors":"Xinyu Li, Dongyang Yao, Xuechao Pan, Jonathan Johannaman, JaeWon Yang, Rachel Webman, Aleksandra Sarcevic, Ivan Marsic, Randall S Burd","doi":"10.1109/RFID.2016.7488002","DOIUrl":null,"url":null,"abstract":"<p><p>We describe a novel and practical activity recognition system for dynamic and complex medical settings using only passive RFID technology. Our activity recognition approach is based on the use of objects that are specific for a given activity. The object-use status is detected from RFID data and the activities are predicted from the statuses of use of different objects. We tagged 10 objects in a trauma room of an emergency department and recorded RFID data for 10 actual trauma resuscitations. More than 20,000 seconds of data were collected and used for analysis. The system achieved a 96% overall accuracy with a 0.74 <i>F</i>-score for detecting use of 10 common resuscitation objects and 95% accuracy with a 0.30 F Score for activity recognition of 10 medical activities.</p>","PeriodicalId":92524,"journal":{"name":"IEEE International Conference on RFID. IEEE International Conference on RFID","volume":"2016 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RFID.2016.7488002","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on RFID. IEEE International Conference on RFID","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RFID.2016.7488002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/6/9 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
Abstract
We describe a novel and practical activity recognition system for dynamic and complex medical settings using only passive RFID technology. Our activity recognition approach is based on the use of objects that are specific for a given activity. The object-use status is detected from RFID data and the activities are predicted from the statuses of use of different objects. We tagged 10 objects in a trauma room of an emergency department and recorded RFID data for 10 actual trauma resuscitations. More than 20,000 seconds of data were collected and used for analysis. The system achieved a 96% overall accuracy with a 0.74 F-score for detecting use of 10 common resuscitation objects and 95% accuracy with a 0.30 F Score for activity recognition of 10 medical activities.